



"""simple docstring"""





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy



@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). '''	,					SCREAMING_SNAKE_CASE__	,					)
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							RobertaConfig
		_lowerCamelCase      	=							'''roberta'''





		def __init__(       self				,     _lowercase )			->    Optional[Any]:







								'''simple docstring'''

								super().__init__(_lowercase )

								snake_case_ :			str            =      RobertaEmbeddings(_lowercase )
								self.init_weights()




@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''	,					SCREAMING_SNAKE_CASE__	,					)
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							RobertaConfig
		_lowerCamelCase      	=							'''roberta'''





		def __init__(       self				,     _lowercase )			->    List[Any]:







								'''simple docstring'''

								super().__init__(_lowercase )
								snake_case_ :			Optional[Any]            =      config.num_labels
								snake_case_ :			Dict            =      config.num_hidden_layers

								snake_case_ :			str            =      DeeRobertaModel(_lowercase )
								snake_case_ :			Dict            =      nn.Dropout(config.hidden_dropout_prob )
								snake_case_ :			List[str]            =      nn.Linear(config.hidden_size				,     self.config.num_labels )





		@add_start_docstrings_to_model_forward(_lowercase )
		def  UpperCAmelCase__				(       self				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=-1				,     _lowercase=False				,     )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			Any            =      self.num_layers
								try:
														snake_case_ :			int            =      self.roberta(
														    _lowercase				,     attention_mask=_lowercase				,     token_type_ids=_lowercase				,     position_ids=_lowercase				,     head_mask=_lowercase				,     inputs_embeds=_lowercase				,     )

														snake_case_ :			str            =      outputs[1]

														snake_case_ :			Union[str, Any]            =      self.dropout(_lowercase )
														snake_case_ :			Tuple            =      self.classifier(_lowercase )
														snake_case_ :			Dict            =      (logits,) + outputs[2:]  # add hidden states and attention if they are here
								except HighwayException as e:
														snake_case_ :			List[Any]            =      e.message
														snake_case_ :			Union[str, Any]            =      e.exit_layer
														snake_case_ :			Dict            =      outputs[0]

								if not self.training:
														snake_case_ :			Dict            =      entropy(_lowercase )
														snake_case_ :			Optional[int]            =      []
														snake_case_ :			Union[str, Any]            =      []
								if labels is not None:
														if self.num_labels == 1:
																				#  We are doing regression
																				snake_case_ :			Dict            =      MSELoss()
																				snake_case_ :			Dict            =      loss_fct(logits.view(-1 )				,     labels.view(-1 ) )
														else:
																				snake_case_ :			Union[str, Any]            =      CrossEntropyLoss()
																				snake_case_ :			Union[str, Any]            =      loss_fct(logits.view(-1				,     self.num_labels )				,     labels.view(-1 ) )

														# work with highway exits
														snake_case_ :			int            =      []
														for highway_exit in outputs[-1]:
																				snake_case_ :			Tuple            =      highway_exit[0]
																				if not self.training:
																										highway_logits_all.append(_lowercase )
																										highway_entropy.append(highway_exit[2] )
																				if self.num_labels == 1:
																										#  We are doing regression
																										snake_case_ :			Optional[int]            =      MSELoss()
																										snake_case_ :			Optional[Any]            =      loss_fct(highway_logits.view(-1 )				,     labels.view(-1 ) )
																				else:
																										snake_case_ :			Optional[int]            =      CrossEntropyLoss()
																										snake_case_ :			Union[str, Any]            =      loss_fct(highway_logits.view(-1				,     self.num_labels )				,     labels.view(-1 ) )
																				highway_losses.append(_lowercase )

														if train_highway:
																				snake_case_ :			Dict            =      (sum(highway_losses[:-1] ),) + outputs
																				# exclude the final highway, of course
														else:
																				snake_case_ :			List[str]            =      (loss,) + outputs
								if not self.training:
														snake_case_ :			Optional[Any]            =      outputs + ((original_entropy, highway_entropy), exit_layer)
														if output_layer >= 0:
																				snake_case_ :			Tuple            =      (
																				    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																				)  # use the highway of the last layer

								return outputs  # (loss), logits, (hidden_states), (attentions), entropy







'''simple docstring'''


class  __lowercase    :  # Public class to implement a graph







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     __a								=       row
     __a								=       col
     __a								=       graph







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     bool:
     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     # Checking all 8 elements surrounding nth element
     __a								=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     __a								=       [-1, 0, 1, -1, 1, -1, 0, 1]
     __a								=       True  # Make those cells visited
     for k in range(8   ):
       if self.is_safe(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   ):
         self.diffs(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:  # And finally, count all islands.
     __a								=       [[False for j in range(self.COL   )] for i in range(self.ROW   )]
     __a								=       0
     for i in range(self.ROW   ):
       for j in range(self.COL   ):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
           count += 1
     return count



